Maximising the benefits of Generative AI for the digital economy

Generative AI can revolutionize the insurance industry by automating underwriting processes. It can analyze vast amounts of data, including policy documents, claims history, and risk factors, to generate accurate risk assessments and pricing models. Generative AI can also aid in fraud detection, leveraging data patterns and anomalies to identify potentially fraudulent claims, mitigating risks and protecting against financial losses.

In 2022, generative AI has gone mainstream, but what many don’t know is that generative models can also have a dark side, they have given rise to a new breed of cyber attacks. GANs have numerous applications, such as creating photorealistic images, videos, and even music. Understanding how artificial intelligence will impact your business and creating a test and implementation strategy will be key to mitigating risk and harnessing the potential for growth. Emerging use cases cover client communication assistance in leasing and property management (such as chatbots to handle tenant queries), floorplan and design generation and summarizing unstructured documents to create reports. Large Language Models, in particular, provide the ability to extract insights from vast amounts of text-based documents in real estate, significantly reducing the complexity of multi-lingual, multi-national operations.

Perspectives on how AI will transform real estate

Generative AI enables businesses to innovate and offer services and solutions that may not be possible otherwise. Whether it is creating unique products through generative design or offering 24/7 customer service through AI-powered chatbots, Generative AI can give businesses a distinct competitive edge. Generative AI can provide highly personalised experiences, enhancing customer satisfaction and loyalty. By understanding and predicting customer preferences and behaviours, Generative AI can create bespoke interactions, recommendations, and services that resonate with each customer.

• This proactive approach not only strengthens the insurer’s position but also enhances customer trust and confidence in the coverage provided.
• There are now technological solutions for almost every aspect of real estate functions, including investment management, design and construction, building and facility operations and portfolio management.
• As policymakers begin to regulate AI, it will become increasingly necessary to distinguish clearly between types of models and their capabilities, and to recognise the unique features of foundation models that may require additional regulatory attention.

These include Microsoft’s Bing Chat[11], Virtual Volunteer by Be My Eyes (a digital assistant for people who are blind or have low vision), and educational apps such as Duolingo Max,[12] Khan Academy’s Khanmigo[13] [14]. Foundation models form the basis of many applications including OpenAI’s ChatGPT, Microsoft’s Bing, and many website chatbots. They similarly underpin many image generation tools such as Midjourney or Adobe Photoshop’s generative fill tools.

Foundation models vs narrow AI

Examples of generative AI tools include ChatGPT, Google Bard, Claude and Midjourney. If you are uploading audio and video, our automated transcription software will prepare your transcript quickly. Once completed, you will get an email notification that your transcript is complete. That email will genrative ai contain a link back to the file so you can access the interactive media player with the transcript, analysis, and export formats ready for you. Chinese regulators said they will take an “inclusive and prudent” attitude towards generative AI services and implement a “graded” regulatory approach.

Generative AI models are created in a way which is similar to how the human brain works, meaning that each time ChatGPT is provided with new training data, the model adapts and adjusts so that the new training data is prioritised when generating an output. This method of training is difficult to reconcile with Article 17 of the GDPR, which provides individuals with right to erasure, as data points cannot be easily traced. If data sources can be traced, to erase data from a training model could compromise the accuracy of the model. Generative AI can be used to automate tasks that would otherwise require manual labor. For example, generative AI can be used to generate images and videos quickly and accurately, which can be used in marketing campaigns or other projects. Midjourney offers powerful capabilities for creating synthetic data and generating realistic content.

Data Protection and Generative AI

“For things like text generation, I could use this today to help generate filler for assets that aren’t really meant to be the focus of the player’s attention, like prop newspapers and such,” says Mills. Games still employ systems that grew from early technological limitations, like dialog or behavior trees. You can’t just drop fancy machine learning into game franchises that have developed without generative AI in mind. Games—in an industry with huge budgets and tight margins—would need total redesigns to accommodate and take advantage of this technology.

A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.

In recent months there have been a number of instances of deepfakes have been created using generative AI. To help me understand the differences and explore some use cases for these categories I was joined on my podcast by Davor Bonaci, CTO and Executive Vice President of DataStax. Bonaci has been with DataStax since the start of the year when it acquired Kaskada, the ML business he co-founded. Before we start, it should be noted that there is often a lot of overlap – specific AI implementations may well fit into more than one of these categories. For the most current information on the most popular and cutting-edge generative AI models, I recommend referring to recent research papers, articles, and AI community discussions. The GAN framework has inspired various extensions, improvements, and applications, leading to numerous variations of GANs.

Consideration should also be given to establishing clear and appropriate accountability lines throughout the company up to senior management, and having in place people with the right skills, expertise, experience and information to support and advise. Recruitment, talent pipeline management and staff training will be aspects to consider in planning for effective AI risk management. As part of any AI procurement your company would also need to understand its responsibilities regarding system use and configuration, the supplier’s business continuity plan and how the unavailability of that platform would affect your business. Some generative AI tools are freely available online – either as stand-alone tools or as products that can integrate into a chain of tools that are provided by multiple developers.

This, in turn, will impact on their people and raise questions in Organisational Design. How many people will need to be recruited and trained to manage the systems company-wide, or within each functional area? Only with time can we answer these questions, but they will require a lot of thought and careful consideration. Next-generation cybersecurity products increasingly incorporate artificial intelligence and machine learning technologies. At LogSentinel, we help companies improve the protection of confidential data and secrets.

In consumer and retail, the technology promises the ability to tailor messages more tightly to individual consumers. And in pharmaceuticals and healthcare, while the impact has been muted so far, there is potential for generative AI to support in areas such as drug discovery. By using generative AI, businesses can generate content that is more accurate and relevant to their customers.

Support for social media management

The system is based on a combination of deep learning techniques and natural language processing, and it has been trained on a massive dataset of human language. Claude is notable for its large context window (the amount of text that the model takes into account when generating a response) of 100,000 tokens. Many of the laws and regulatory principles referenced above (see section 2 above) include requirements regarding governance, oversight and documentation. In addition, sector-specific frameworks for genrative ai governance and oversight can affect what ‘responsible’ AI use and governance means in certain contexts. Additionally, laws that apply to specific types of technology, such as facial recognition software, online recommender technology or autonomous driving systems, will impact how AI should be deployed and governed in respect of those technologies. Generative AI empowers insurers to automate traditionally time-consuming processes, enabling them to focus on strategic initiatives and higher-value tasks.

But perhaps the most intriguing capability of the technology in the media sector is its ability to replicate actors’ faces and voices. For instance, in the last few months online, AI has been used to mimic the voices of famous singers performing different artists’ songs. So far, Freddie Mercury’s voice has been used to sing Celine Dion’s My Heart Will Go On, while an AI Taylor Swift has performed Kanye West’s Heartless, among many other examples. Explaining how a generative AI system operates to generate output becomes increasingly challenging as the level of sophistication of these systems increases. The challenge of explicability can be further complicated when the AI technology is supplied by another provider or a chain of providers who themselves lack the visibility of how such system operates or functions.

Nevertheless, GlobalData argues that despite fears that generative AI will threaten creative jobs, AI will mostly take on more monotonous and mundane tasks, freeing creatives to focus on higher-value activity. Among the many emerging applications of Artificial Intelligence, Generative AI is one of the most interesting and at the same time most disturbing, given that creative activity has always been thought of as a distinctive feature of the human mind. Generative Artificial Intelligence is an application of this technology that is as promising as it is disturbing, which requires companies to adopt an approach based on a strong ethical foundation. Regulating explicable – or “explainable” – AI models is completely different when it comes to AI models that cannot be explained or interpreted; the regulatory framework will only apply to their inputs and outputs.